关键词: 黄金分割, 粒子群算法, 混沌算法, X条件云发生器

Abstract: To improve the low accuracy and premature convergent in traditional Particle Swarm Optimization（PSO） algorithm, the chaos cloud particle swarm algorithm based on golden section evaluation criteria（CCGPSO） is proposed in this paper. This method divides the particle swarm into standard particle, chaos cloud particle and cloud particle using the golden section judge principles according to fitness level, every sub swarm particle has respective different algorithm operations. The golden section enables particle swarm to search the entire solution space, solves the problems of easily falling into local optimum and low accuracy in basic PSO. This paper chooses four reference functions to have a test and compared with chaos cloud particle swarm optimization （CCPSO）. The simulation results demonstrate CCGPSO has high optimization precision and convergence speed.

Key words: golden section, Particle Swarm Optimization（PSO）； chaos optimization, X condition cloud generator